Method and system for generating composite PET-CT image based on non-attenuation-corrected PET image

ABSTRACT

The present disclosure discloses a method and a system for generating a composite PET-CT image based on a non-attenuation-corrected PET image. The method includes: constructing a first generative adversarial network and a second generative adversarial network; obtaining a mapping relationship between a non-attenuation-corrected PET image and an attenuation-corrected PET image by training the first generative adversarial network; obtaining a mapping relationship between the attenuation-corrected PET image and a CT image by training the second generative adversarial network; and generating the composite PET-CT image by utilizing the obtained mapping relationships. According to the present disclosure, a high-quality PET-CT image can be directly composited from a non-attenuation-corrected PET image, and medical costs can be reduced for patients, and radiation doses applied to the patients in examination processes can be minimized.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a 371 application of International PCT applicationserial no. PCT/CN2020/074625, filed on Feb. 10, 2020. The entirety ofeach of the above-mentioned patent applications is hereby incorporatedby reference herein and made a part of this specification.

BACKGROUND Technical Field

The present disclosure relates to the field of medical image processingtechnologies, and more particularly, to a method and a system forgenerating a composite positron emission tomography-computed tomography(PET-CT) image based on a non-attenuation-corrected PET image.

Description of Related Art

Positron emission tomography (PET) is a non-invasive imaging technology.After a specific contrast agent such as ¹⁸F-fluorodeoxyglucose (FDG) isinjected, data are acquired with the aid of a PET scanner and undergo aseries of post-processing. A reconstructed PET image can clearly reflectmetabolic levels of patients' tissues or organs, and thus can be usedclinically for early screening of tumors and post-operative stagingdiagnosis of the patients. However, the PET imaging only reflects thefunctional information of tissues or organs. It also requires additionalcomputer tomography (CT) to perform attenuation correction of the PETimaging to provide additional human anatomical information to locatelesion positions, which inevitably brings the patients additionalionizing radiation, and thus potentially increases cancer risks of thepatients. Therefore, research and development of an effective methodcapable of generating pseudo-CT from medical images of other modalities,such as Magnetic Resonance Images (MRI), so as to replace additionalanatomical imaging during PET scanning, have important scientificsignificance and application prospect for reducing the radiation dosessuffered by the patients and reducing high costs of PET/CT examination.

In the existing technologies, technical solutions for generating CTimages mainly have the following problems. Directly performing a PET/CTexamination is not only more expensive, but also brings more radiationdoses for the patients during the examination process. The method forestimating attenuation correction of PET by compositing CT using MRI hasthe disadvantage of longer time required for acquiring MRI sequences.Furthermore, the accuracy of manual data registration has a directimpact on post-imaging quality. Moreover, involuntary movement of thepatients' limbs during PET/MRI examination may cause truncationartifacts, etc.

SUMMARY

An objective of the present disclosure is to provide a method and asystem for generating a composite PET-CT image based on anon-attenuation-corrected PET image, which is a new technical solutionfor implementation of compositing a PET-CT image from anon-attenuation-corrected PET image based on a deep learning method.

According to a first aspect of the present disclosure, there is provideda method for generating a composite PET-CT image based on anon-attenuation-corrected PET image, which includes following steps:

constructing a first generative adversarial network containing a firstgenerator and a first discriminator, and constructing a secondgenerative adversarial network containing a second generator and asecond discriminator;

performing a feature extraction by using the non-attenuation-correctedPET image as an input of the first generator to obtain a compositeattenuation-corrected PET image, and carrying out a training by usingthe composite attenuation-corrected PET image as an input of the firstdiscriminator and by using the attenuation-corrected PET image as areference image of the first discriminator to obtain a mappingrelationship G₁ between the non-attenuation-corrected PET image and theattenuation-corrected PET image;

performing a feature extraction by using the attenuation-corrected PETimage as an input of the second generator to obtain a composite CTimage, and carrying out a training by using the as a reference image ofthe second discriminator to obtain a mapping relationship G₂ between theattenuation-corrected PET image and the CT image; and

generating the composite PET/CT image from the non-attenuation-correctedPET image by utilizing the mapping relationship G₁ and the mappingrelationship G₂.

In an embodiment, the first generative adversarial network and thesecond generative adversarial network have the same or different networkstructures.

In an embodiment, the first generator and the second generator have thesame network structure, successively including a plurality ofconvolutional layers and pooling layers, a plurality of residual blocks,and a plurality of deconvolutional layers corresponding to the pluralityof convolutional layers. The convolutional layer and the correspondingdeconvolutional layer employ a hop connection.

In an embodiment, the first discriminator and the second discriminatoremploy a fully-connected neural network to determine whether an inputimage comes from an output of a corresponding generator or from thereference image.

In an embodiment, the first discriminator and the second discriminatorcompare, based on a distance, a distribution similarity between anoutput image from the corresponding generator and the reference image.

In an embodiment, the first generative adversarial network and thesecond generative adversarial network are based on WassersteinGenerative Adversarial Network, and an adversarial loss is defined as:

${\min\limits_{G}\max\limits_{D}{L_{WGAN}\left( {G,D} \right)}} = {{- {E_{x}\left\lbrack {D(x)} \right\rbrack}} + {E_{z}\left\lbrack {D\left( {G(x)} \right)} \right\rbrack} + {\lambda{E_{\hat{x}}\left\lbrack \left( {{{\nabla_{\hat{x}}{D\left( \hat{x} \right)}}}^{2} - 1} \right)^{2} \right\rbrack}}}$

wherein λ represents a hyperparameter, X represents an input image, G(x)represents an output of the generator, E(·) used for calculating an EMdistance, {circumflex over (X)} represents a sample randomly selectedfrom a real image and the reference image, and D(·) represents adiscrimination process of the discriminator.

In an embodiment, in a training process, a total objective function isset as including one or more of an adversarial loss term and a meansquare error loss term, an image gradient loss teini, a multiscalecontent loss term, and a structural similarity loss term.

In an embodiment, in the training process, the total objective functionis set as:

${L\left( {I_{AC},I_{sAC}} \right)} = {{\lambda_{o} \cdot {\min\limits_{G}{\max\limits_{D}{L_{WGAN}\left( {G,D} \right)}}}} + {\lambda_{mse} \cdot {L_{mse}\left( {I_{AC},I_{sAC}} \right)}} + {\lambda_{gdl} \cdot {L_{gdl}\left( {I_{AC},I_{sAC}} \right)}} + {\lambda_{con{tent}} \cdot {L_{content}\left( {I_{AC},I_{sAC}} \right)}} + {\lambda_{ssim} \cdot {L_{ssim}\left( {I_{AC},I_{sAC}} \right)}}}$

wherein I_(AC) represents a real attenuation-corrected PET image,I_(sAC) represents a generated attenuation-corrected PET image, λ₀represents a weight of the adversarial loss, λ_(mse) represents a weightof the mean square error, λ_(gdl) represents a weight of the imagegradient loss, λ_(content) represents a weight of the multiscale contentloss, and λ_(ssim) represents a weight of the structural similarityloss.

According to a second aspect of the present disclosure, there isprovided a system for generating a composite PET-CT image based on anon-attenuation-corrected PET image. The system includes:

a network model constructing unit, configured to construct a firstgenerative adversarial network containing a first generator and a firstdiscriminator, and construct a second generative adversarial networkcontaining a second generator and a second discriminator;

a first training unit, configured to perform a feature extraction byusing the non-attenuation-corrected PET image as an input of the firstgenerator to obtain a composite attenuation-corrected PET image, andcarry out a training by using the composite attenuation-corrected PETimage as an input of the first discriminator and by using theattenuation-corrected PET image as a reference image of the firstdiscriminator to obtain a mapping relationship G₁ between thenon-attenuation-corrected PET image and the attenuation-corrected PETimage;

a second training unit, configured to perform a feature extraction byusing the attenuation-corrected PET image as an input of the secondgenerator to obtain a composite CT image, and carry out a training byusing the composite CT image as an input of the second discriminator andby using the trained CT image as a reference image of the seconddiscriminator to obtain a mapping relationship G₂ between theattenuation-corrected PET image and the CT image; and

an image compositing unit, configured to generate the composite PET/CTimage from the non-attenuation-corrected PET image by utilizing themapping relationship G₁ and the mapping relationship G².

Compared with the existing technologies, the present disclosure has thefollowing advantages. Directly generating a composite PET-CT image byusing an existing non-attenuation-corrected PET image eliminates thestep of performing an attenuation correction of PET imaging bycompositing a CT image using MRI, which provides a new idea forsubsequent practical applications. In the event that neither PET/MRI norPET/CT have been popularized, the present disclosure provides aneffective shortcut to compositing the PET/CT image, which not only canreduce medical expenses for the patients, but also can minimize theradiation doses applied to the patients in the examination processes.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings herein are incorporated in and constitute apart of this specification, illustrate embodiments of the presentdisclosure and, together with the specification, serve to explain theprinciples of the present disclosure.

FIG. 1 is a flowchart of a method for generating a composite PET-CTimage based on a non-attenuation-corrected PET image according to anembodiment of the present disclosure.

FIG. 2 is a diagram showing a network model for implementing the methodfor generating a composite PET-CT image based on anon-attenuation-corrected PET image according to an embodiment of thepresent disclosure.

FIG. 3 is a diagram showing an experiment effect according to anembodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Various exemplary embodiments of the present disclosure will now bedescribed in detail with reference to the accompanying drawings. It isto be noted that the relative arrangement, numerical expressions, andnumerical values of the components and steps set forth in theseembodiments do not limit the scope of the present disclosure unlessotherwise specifically stated.

The following description of at least one exemplary embodiment isactually merely illustrative, and in no way serves as any limitation onthe present disclosure and application or use thereof.

Technologies, methods and equipment known to those of ordinary skill inthe related art may not be discussed in detail, but where appropriate,the technologies, methods and equipment should be considered as part ofthe specification.

In all examples shown and discussed herein, any specific values shouldbe interpreted as merely exemplary and not limiting. Therefore, otherexamples of the exemplary embodiment may have different values.

It is to be noted that similar reference numerals and letters indicatesimilar items in the following accompanying drawings. Therefore, once anitem is defined in one drawing, there is no need to discuss this itemfurther in subsequent drawings.

In short, the present disclosure designs a two-stage network to solvethe problem of attenuation correction of a PET image and reduction ofradiation doses applied to patients during examination. The networkincludes two stages as below. In the first stage,self-attenuation-correction of a non-attenuation-corrected PET image iscompleted, and a composite attenuation-corrected PET image is obtained.In the second stage, a corresponding CT image is estimated by using thecomposite attenuation-corrected PET image obtained in the first stage.After these two stages, a PET/CT image may be generated by using a setof non-attenuation-corrected PET images.

To achieve the above object, in one embodiment, a composite PET/CT imageis generated from a non-attenuation-corrected PET image by using animproved Wasserstein Generative Adversarial Network. Referring to FIG.1, the method of the present disclosure specifically includes thefollowing steps.

In Step S110, a generative adversarial network model containing agenerator and a discriminator is constructed.

The network model provided by this embodiment of the present disclosureincludes two generative adversarial networks, and each generativeadversarial network includes a generator network (or generator forshort) and a discriminator network (or discriminator for short).Implementation of generation of the composite, PET/CT image is dividedinto two stages as below. The first stage is a training stage, atraining network is composed of the generator and the discriminator. Thesecond stage is a testing stage, where the composite PET/CT image isgenerated from the non-attenuation-corrected PET image by using thenetwork trained in the first stage.

Specifically, as shown in FIG. 2, in one embodiment, the generatornetwork includes five convolutional layers (including a pooling layer,which employs, for example, max-pooling), nine residual blocks, fivedeconvolutional layers and one convolutional layer from left to right,and the discriminator network includes four convolutional layers(including a pooling layer) and two fully-connected layers in sequence,where each residual block includes two convolutional layers.

In conjunction with FIG. 2, multiple feature extractions are performedon an input image by the five convolutional layers (convolutionoperation is used to extract features, the max-pooling operation is usedto reduce an image size, and an activation function is used to increasethe nonlinearity of the network). Next, after a data stream passesthrough the nine residual blocks and the five deconvolutional layers, aresolution of the image is restored to the size of the input image, andthen an output image is converted. For example, the size of aconvolution kernel used by the entire network is 3×3, and the number offilters used in an encoding section is 64, 128, 256, 512, and 512respectively.

The discriminator network employs, for example, a fully-connected neuralnetwork (FCN) to determine whether the input image comes from the outputof the generator or from a reference image. In the embodiment of thepresent disclosure, the discriminator network does not employ aclassification probability, but employs a special feature-based distanceto measure the difference between the output image of the generator andthe reference image. Specifically, instead of using the activationfunction based on real or fake classification, the discriminatordirectly uses two fully-connected layers to receive and output advancedfeatures, and then calculates a distance between a real image and thereference image on this basis. The distance is used to compare adistribution similarity between the output image from the generator andthe reference image, and can provide meaningful gradient information.

For the first generative adversarial network, a feature extraction isperformed by using a trained non-attenuation-corrected PET image as aninput of the generator to obtain a composite attenuation-corrected PETimage, and the composite attenuation-corrected PET image is used as aninput of the discriminator, and a trained attenuation-corrected PETimage is used as the reference image of the discriminator.

For the second generative adversarial network, a feature extraction isperformed by using a trained attenuation-corrected PET image as an inputof the generator to obtain a composite CT image, and the composite CTimage is used as an input of the discriminator, and a trained CT imageis used as the reference image of the discriminator.

It is to be noted that the first generative adversarial network and thesecond generative adversarial network may be constructed into the sameor different network structures. For example, the generator networks ofthe two generative adversarial networks may be designed to havedifferent number of convolutional layers or different number of residualblocks, etc. For another example, the discriminators of the twogenerative adversarial networks may use different classificationmethods. A person skilled in the art may make appropriate modificationsto the network model according to factors such as requirements for aprocessing speed and a processing accuracy, which is not limited by thepresent disclosure.

In Step S120, a loss function is designed for the generative adversarialnetwork.

The Wasserstein Generative Adversarial Network is employed in theembodiment of the present disclosure, and the adversarial loss of thenetwork may be defined as:

$\begin{matrix}\begin{matrix}{{\min\limits_{G}\max\limits_{D}{L_{WGAN}\left( {G,D} \right)}} = {{- {E_{x}\left\lbrack {D(x)} \right\rbrack}} + {E_{z}\left\lbrack {D\left( {G(x)} \right)} \right\rbrack} + {\lambda{E_{\hat{x}}\left\lbrack \left( {{{\nabla_{\hat{x}}{D\left( \hat{x} \right)}}}^{2} - 1} \right)^{2} \right\rbrack}}}} & \;\end{matrix} & (1)\end{matrix}$

wherein λ represents a hyperparameter, which may be set as 10 based onexperiences, X represents the input image, G(x) represents the output ofthe generator, E(·) is used for calculating an Earth-Mover (EM)distance, {circumflex over (x)} represents a sample randomly selectedfrom a real image and the reference image in a certain proportion, andD(·) represents a discrimination process of the discriminator.

In another embodiment, to make up for the traditional problem of imageblur caused by a distance L₂, the multiscale content loss, the imagegradient loss and the structural similarity loss are introduced. Forexample, a total objective function is defined as follows:

$\begin{matrix}{{L\left( {I_{AC},I_{sAC}} \right)} = {{\lambda_{o} \cdot {\min\limits_{G}{\max\limits_{D}{L_{WGAN}\left( {G,D} \right)}}}} + {\lambda_{mse} \cdot {L_{mse}\left( {I_{AC},I_{sAC}} \right)}} + {\lambda_{gdl} \cdot {L_{gdl}\left( {I_{AC},I_{sAC}} \right)}} + {\lambda_{con{tent}} \cdot {L_{content}\left( {I_{AC},I_{sAC}} \right)}} + {\lambda_{ssim} \cdot {L_{ssim}\left( {I_{AC},I_{sAC}} \right)}}}} & (2)\end{matrix}$

wherein I_(AC) represents a real attenuation-corrected PET image,I_(sAC) represents a generated attenuation-corrected PET image,represents a weight of the adversarial loss, λ_(mse) represents a weightof the mean square error, λ_(gdl) represents a weight of the imagegradient loss, λ_(content) represents a weight of the multiscale contentloss, and λ_(ssim) represents a weight of the structural similarityloss. These hyperparameters may be set as appropriate values accordingto a plurality of experiment effects.

In the training process, an optimal solution is obtained by minimizingthe total objective function, that is, a high-quality output image isobtained.

It is to be noted that those skilled in the art may change the aboveobjective function according to an actual application. For example, inaddition to the adversarial loss, one or more of the mean square errorloss term, the image gradient loss term, the multiscale content loss teiand the structural similarity loss term may be selected, without havingto include all of the above loss items.

In Step S130, the generative adversarial network is trained to obtain amapping relationship between the non-attenuation-corrected PET image andthe attenuation-corrected PET image and a mapping relationship betweenthe attenuation-corrected PET image and the CT image.

For example, the generator and the discriminator are separately trainedby extracting, from a data set of the non-attenuation-corrected PETimage, the attenuation-corrected PET image and the CT image, a batch ofpaired image pairs as network input. Through training, the mappingrelationships G₁ and G₂ between the input image and the reference imageare obtained, wherein G₁ represents the mapping from thenon-attenuation-corrected PET image to the attenuation-corrected PETimage, and G₂ represents the mapping from the attenuation-corrected PETimage to the CT image.

In the process of jointly training the generator and the discriminatorof the generative adversarial network, an optimizer in the prior art maybe used for optimization. For example, an Adam optimization algorithm isused for optimization, and an exponentially decreasing learning rate isused.

This method of network optimization using deep learning in combinationwith a plurality of loss functions can implementself-attenuation-correction of the non-attenuation-corrected PET imageand achieve good results.

In Step S140, the composite PET/CT image is generated based on theobtained mapping relationships.

After the mapping relationships G₁ and G₂ are obtained, the compositePET/CT image may be directly generated based on the existingnon-attenuation-corrected PET image. That is, the compositeattenuation-corrected PET image is obtained by inputting thenon-attenuation-corrected PET image to a trained model G₁, and then thecomposite CT image (still referring to FIG. 2) is obtained by inputtingthe composite attenuation-corrected PET image to a trained model G₂.

Correspondingly, the present disclosure provides a system for generatinga composite PET-CT image based on a non-attenuation-corrected PET image,which is configured to implement one or more aspects of the abovemethod. For example, the system includes: a network model constructingunit, configured to construct a first generative adversarial networkcontaining a first generator and a first discriminator, and construct asecond generative adversarial network containing a second generator anda second discriminator; a first training unit, configured to perform afeature extraction by using the non-attenuation-corrected PET image asan input of the first generator to obtain a compositeattenuation-corrected PET image, and carry out a training by using thecomposite attenuation-corrected PET image as an input of the firstdiscriminator and by using the attenuation-corrected PET image as areference image of the first discriminator to obtain a mappingrelationship G₁ between the non-attenuation-corrected PET image and theattenuation-corrected PET image; a second training unit, configured toperform a feature extraction by using the attenuation-corrected PETimage as an input of the second generator to obtain a composite CTimage, and carry out a training by using the composite CT image as aninput of the second discriminator and by using the trained CT image as areference image of the second discriminator to obtain a mappingrelationship G₂ between the attenuation-corrected PET image and the CTimage; and an image compositing unit, configured to generate thecomposite PET/CT image from the non-attenuation-corrected PET image byutilizing the mapping relationship and the mapping relationship G₂. Inthe system provided by the present disclosure, each module may beimplemented by using a processor or a logic circuit.

It is to be noted that the present disclosure not only is applicable toreplacing the existing PET/CT system, but also is applicable to aPET/MRI system after appropriate modification.

In summary, according to the present disclosure, two sets of networksare separately trained using an improved deep Wasserstein GenerativeAdversarial Network (a coupled residual network) to learn an end-to-endnon-linear mapping relationship between the non-attenuation-correctedPET image and the attenuation-corrected PET image, and theattenuation-corrected PET image and the CT image. By combining variousloss functions (such as the image gradient loss, the content loss and soon) to limit the output, the distortion of images generated by thegenerative adversarial network is effectively reduced, and the detailedinformation (such as edges, etc.) of the image is largely retained,thereby activating the network to generate high-quality images. Thepresent disclosure implements the self-attenuation-correction of the PETimage, and the generated CT image can provide more accurate anatomicallocation for the diagnosed PET imaging.

It has been verified that a clearer high-quality image can be obtainedby using the present disclosure, referring to the comparison ofexperimental results on different slices as shown in FIG. 3, wherein (a)shows a reference CT image, (b) shows a composite CT image, (c) shows areference PET image, and (d) shows a composite PET image.

The present disclosure may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium having computer readable program instructionsthereon for causing a processor to carry out aspects of the presentdisclosure.

The computer readable storage medium may be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium include the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Thecomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may includecopper transmission cables, optical fiber transmission, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

The computer program instructions for carrying out operations of thepresent disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, finnware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including anobject-oriented programming language such as Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In a scenario involvedwith the remote computer, the remote computer may be coupled to theuser's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or may be coupled to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described with reference toflowcharts and/or block diagrams according to the method, apparatus(system) and a computer program product of the embodiments of thepresent disclosure. It is to be understood that each block of theflowcharts and/or block diagrams, and combinations of blocks in theflowcharts and/or block diagrams, can be implemented by the computerreadable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat these instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in one or more blocks in theflowcharts and/or block diagrams. These computer readable programinstructions may also be stored in a computer readable storage mediumthat can direct a computer, a programmable data processing apparatus,and/or other devices to function in a particular manner, such that thecomputer readable medium having instructions stored therein includes anarticle of manufacture including instructions which implement aspects ofthe function/act specified in one or more blocks in the flowchartsand/or block diagrams.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in one or more blocks in the flowcharts and/orblock diagrams.

The flowcharts and block diagrams in the accompanying drawingsillustrate architectures, functions and operations of possibleimplementations of systems, methods, and computer program productsaccording to a plurality of embodiments of the present disclosure. Inthis regard, each block in the flowcharts or block diagrams mayrepresent a module, a program segment, or a portion of instructions,which includes one or more executable instructions for functions denotedby the blocks may occur in a sequence different from the sequences shownin the accompanying drawings. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in a reverse sequence, depending uponthe functions involved. It is also to be noted that each block in theblock diagrams and/or flowcharts and/or a combination of the blocks inthe block diagrams and/or flowcharts may be implemented by aspecial-purpose hardware-based system executing specific functions oracts, or by a combination of a special-purpose hardware and computerinstructions. It is well known to those skilled in the art thatimplementations by means of hardware, implementations by means ofsoftware and implementations by means of software in combination withhardware are equivalent.

The descriptions of the various embodiments of the present disclosurehave been presented above for purposes of illustration, but are notintended to be exhaustive or limited to the embodiments disclosed.Therefore, it is apparent to an ordinary skilled person in the art thatmodifications and variations could be made without departing from thescope and spirit of the embodiments. The terminology used herein ischosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein. The scope of the presentdisclosure is limited by the appended claims.

What is claimed is:
 1. A method for generating a composite PET-CT imagebased on a non-attenuation-corrected PET image, comprising steps of:constructing a first generative adversarial network containing a firstgenerator and a first discriminator, and constructing a secondgenerative adversarial network containing a second generator and asecond discriminator; performing a feature extraction by using thenon-attenuation-corrected PET image as an input of the first generatorto obtain a composite attenuation-corrected PET image, and carrying outa training by using the composite attenuation-corrected PET image as aninput of the first discriminator and by using the attenuation-correctedPET image as a reference image of the first discriminator to obtain amapping relationship G₁ between the non-attenuation-corrected PET imageand the attenuation-corrected PET image; performing a feature extractionby using the attenuation-corrected PET image as an input of the secondgenerator to obtain a composite CT image, and carrying out a training byusing the composite CT image as an input of the second discriminator andby using the trained CT image as a reference image of the seconddiscriminator to obtain a mapping relationship G₂ between theattenuation-corrected PET image and the CT image; and generating acomposite PET/CT image from the non-attenuation-corrected PET image byutilizing the mapping relationship G₁ and the mapping relationship G₂,wherein the first generative adversarial network and the secondgenerative adversarial network are based on Wasserstein GenerativeAdversarial Network, and an adversarial loss is defined as:${\min\limits_{G}{\max\limits_{D}{L_{WGAN}\left( {G,D} \right)}}} = {{- {E_{x}\left\lbrack {D(x)} \right\rbrack}} + {E_{z}\left\lbrack {D\left( {G(x)} \right)} \right\rbrack} + {\lambda{E_{\hat{x}}\left\lbrack \left( {{{\nabla_{\hat{x}}{D\left( \hat{x} \right)}}}^{2} - 1} \right)^{2} \right\rbrack}}}$wherein λ represents a hyperparameter, X represents an input image, G(x)represents an output of the generator, E(·) is used for calculating anEM distance, {circumflex over (X)} represents a sample randomly selectedfrom a real image and the reference image, and D(·) represents adiscrimination process of the discriminator.
 2. The method forgenerating the composite PET-CT image based on thenon-attenuation-corrected PET image according to claim 1, wherein thefirst generative adversarial network and the second generativeadversarial network have the same or different network structures. 3.The method for generating the composite PET-CT image based on thenon-attenuation-corrected PET image according to claim 1, wherein thefirst generator and the second generator have the same networkstructure, successively comprising a plurality of convolutional layersand pooling layers, a plurality of residual blocks, and a plurality ofdeconvolutional layers corresponding to the plurality of convolutionallayers, and wherein the convolutional layer and the correspondingdeconvolutional layer employ a hop connection.
 4. The method forgenerating the composite PET-CT image based on thenon-attenuation-corrected PET image according to claim 1, wherein thefirst discriminator and the second discriminator employ afully-connected neural network to determine whether an input image comesfrom an output of a corresponding generator or from the reference image.5. The method for generating the composite PET-CT image based on thenon-attenuation-corrected PET image according to claim 4, wherein thefirst discriminator and the second discriminator compare, based on adistance, a distribution similarity between an output image from thecorresponding generator and the reference image.
 6. The method forgenerating the composite PET-CT image based on thenon-attenuation-corrected PET image according to claim 1, wherein in atraining process, a total objective function is set as comprising one ormore of an adversarial loss term and a mean square error loss term, animage gradient loss term, a multiscale content loss term, and astructural similarity loss term.
 7. The method for generating thecomposite PET-CT image based on the non-attenuation-corrected PET imageaccording to claim 6, wherein in the training process, the totalobjective function is set as:${L\left( {I_{AC},I_{sAC}} \right)} = {{\lambda_{o} \cdot {\min\limits_{G}{\max\limits_{D}{L_{WGAN}\left( {G,D} \right)}}}} + {\lambda_{mse} \cdot {L_{mse}\left( {I_{AC},I_{sAC}} \right)}} + {\lambda_{gdl} \cdot {L_{gdl}\left( {I_{AC},I_{sAC}} \right)}} + {\lambda_{con{tent}} \cdot {L_{content}\left( {I_{AC},I_{sAC}} \right)}} + {\lambda_{ssim} \cdot {L_{ssim}\left( {I_{AC},I_{sAC}} \right)}}}$wherein I_(AC) represents a real attenuation-corrected PET image,I_(sAC) represents a generated attenuation-corrected PET image, λ₀represents a weight of the adversarial loss, λ_(mse) represents a weightof the mean square error, λ_(gdl) represents a weight of the imagegradient loss, λ_(content) represents a weight of the multiscale contentloss, and λ_(ssim) represents a weight of the structural similarityloss.
 8. A system for generating a composite PET-CT image based on anon-attenuation-corrected PET image, comprising: a network modelconstructing unit, configured to construct a first generativeadversarial network containing a first generator and a firstdiscriminator, and construct a second generative adversarial networkcontaining a second generator and a second discriminator; a firsttraining unit, configured to perform a feature extraction by using thenon-attenuation-corrected PET image as an input of the first generatorto obtain a composite attenuation-corrected PET image, and carry out atraining by using the composite attenuation-corrected PET image as aninput of the first discriminator and by using the attenuation-correctedPET image as a reference image of the first discriminator to obtain amapping relationship G₁ between the non-attenuation-corrected PET imageand the attenuation-corrected PET image; a second training unit,configured to perform a feature extraction by using theattenuation-corrected PET image as an input of the second generator toobtain a composite CT image, and carry out a training by using thecomposite CT image as an input of the second discriminator and by usingthe trained CT image as a reference image of the second discriminator toobtain a mapping relationship G₂ between the attenuation-corrected PETimage and the CT image; and an image compositing unit, configured togenerate the composite PET/CT image from the non-attenuation-correctedPET image by utilizing the mapping relationship G₁ and the mappingrelationship G₂, wherein the first generative adversarial network andthe second generative adversarial network are based on WassersteinGenerative Adversarial Network, and an adversarial loss is defined as:${\min\limits_{G}{\max\limits_{D}{L_{WGAN}\left( {G,D} \right)}}} = {{- {E_{x}\left\lbrack {D(x)} \right\rbrack}} + {E_{z}\left\lbrack {D\left( {G(x)} \right)} \right\rbrack} + {\lambda{E_{\hat{x}}\left\lbrack \left( {{{\nabla_{\hat{x}}{D\left( \hat{x} \right)}}}^{2} - 1} \right)^{2} \right\rbrack}}}$wherein λ represents a hyperparameter, X represents an input image, G(x)represents an output of the generator, E(·) is used for calculating anEM distance, {circumflex over (X)} represents a sample randomly selectedfrom a real image and the reference image, and D(·) represents adiscrimination process of the discriminator.
 9. A computer readablestorage medium, storing a computer program, wherein when being executedby a processor, the program implements steps of the method according toclaim 1.